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Gagan Bansal
Gagan Bansal
Microsoft Research
Verified email at microsoft.com - Homepage
Title
Cited by
Cited by
Year
AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework
Q Wu, G Bansal, J Zhang, Y Wu, B Li, EE Zhu, L Jiang, X Zhang, S Zhang, ...
COLM, 2024
6282024
Does the whole exceed its parts? The effect of AI explanations on complementary team performance
G Bansal, T Wu, J Zhou, R Fok, B Nushi, E Kamar, MT Ribeiro, D Weld
CHI, 1-16, 2021
6172021
Beyond accuracy: The role of mental models in human-AI team performance
G Bansal, B Nushi, E Kamar, WS Lasecki, DS Weld, E Horvitz
HCOMP 7, 2-11, 2019
4932019
Updates in human-AI teams: Understanding and addressing the performance/compatibility tradeoff
G Bansal, B Nushi, E Kamar, DS Weld, WS Lasecki, E Horvitz
AAAI 33 (01), 2429-2437, 2019
3592019
The challenge of crafting intelligible intelligence
DS Weld, G Bansal
Communications of the ACM 62 (6), 70-79, 2019
350*2019
Is the most accurate AI the best teammate? Optimizing AI for teamwork
G Bansal, B Nushi, E Kamar, E Horvitz, DS Weld
AAAI 35 (13), 11405-11414, 2021
1802021
Reading between the lines: Modeling user behavior and costs in AI-assisted programming
H Mozannar, G Bansal, A Fourney, E Horvitz
CHI, 2024
105*2024
Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations
V Chen, QV Liao, JW Vaughan, G Bansal
CSCW, 2023
952023
Hierarchical summarization: Scaling up multi-document summarization
J Christensen, S Soderland, G Bansal
ACL, 902-912, 2014
772014
Generation probabilities are not enough: Exploring the effectiveness of uncertainty highlighting in AI-powered code completions
H Vasconcelos, G Bansal, A Fourney, QV Liao, JW Vaughan
ToCHI, 2024
48*2024
A coverage-based utility model for identifying unknown unknowns
G Bansal, D Weld
AAAI 32 (1), 2018
452018
Do explanations help users detect errors in open-domain QA? An evaluation of spoken vs. visual explanations
AV González, G Bansal, A Fan, Y Mehdad, R Jia, S Iyer
ACL, 1103-1116, 2021
43*2021
When to Show a Suggestion? Integrating Human Feedback in AI-Assisted Programming
H Mozannar, G Bansal, A Fourney, E Horvitz
AAAI, 2023
262023
Emerging perspectives in human-centered machine learning
G Ramos, J Suh, S Ghorashi, C Meek, R Banks, S Amershi, R Fiebrink, ...
Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing …, 2019
262019
Aligning Offline Metrics and Human Judgments of Value for Code Generation Models
V Dibia, A Fourney, G Bansal, F Poursabzi-Sangdeh, H Liu, S Amershi
ACL, 2023
24*2023
Technology-enabled disinformation: Summary, lessons, and recommendations
J Akers, G Bansal, G Cadamuro, C Chen, Q Chen, L Lin, P Mulcaire, ...
arXiv preprint arXiv:1812.09383, 2018
242018
Data staining: A method for comparing faithfulness of explainers
J Sippy, G Bansal, DS Weld
Proc. of ICML Workshop on Human Interpretability in Machine Learning (WHI), 2020
122020
Explanatory dialogs: Towards actionable, interactive explanations
G Bansal
Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, 356-357, 2018
92018
Workshop on Trust and Reliance in AI-Human Teams (TRAIT)
G Bansal, Z Buçinca, K Holstein, J Hullman, AM Smith-Renner, S Stumpf, ...
Extended Abstracts at CHI, 1-6, 2023
32023
Embedded Attributes for Modifying Behaviors of Generative AI Systems
SA Amershi, A Fourney, VC Dibia, G Bansal
US Patent App. 18/197,878, 2024
2024
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